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基于Monte-Carlo方法的井下无线传感网络定位算法 被引量:2

Monte-Carlo method based localization algorithm for underground wireless sensor network
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摘要 在无线传感器网络应用当中,位置数据向来是关键信息之一。怎样用最小的代价,使得定位算法更加稳定健壮、更精确、更高效,是目前无线传感网定位算法追求的一个方向。因为无线传感网络有着很强的应用相关性,Monte-Carlo中心定位算法以井下环境为背景,设计的一种基于Monte-Carlo算法的改进的定位算法,定位方法简单,定位计算量小。最后通过实验将该算法和Monte-Carlo算法进行了仿真,结果显示在井下环境条件下,该算法有很强的稳定性和更好的精度。 For most wireless sensor network applications, position data is one of key information. How to make the positioning algorithm more stable and robust, more accurate, more efficient with the minimum cost is a direction which wireless sensor net- work localization algorithm to pursue. Applications of wireless sensor network are attached great relevence to application. It takes tunnel environment as the background, designs an improved localization algorithm based on the Monte-Carlo localization (Center Monte-Carlo Location, CMCL), CMCL method is simple and uses bitty computation. Compared and analyzed the CMCL and Monte-Carlo localization though the experiment of simulation, the result reveals that the CMCL has strong stability and better accuracy.
出处 《计算机工程与应用》 CSCD 2013年第23期81-85,234,共6页 Computer Engineering and Applications
基金 国家自然科学基金(No.60975074)
关键词 无线传感网 MONTE Carlo 定位算法 wireless sensor network Monte-Carlo localization algorithm
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参考文献9

  • 1高隽,蹦昭.图像理解理论与方法[M]北京:科学出版社,2009.
  • 2Wan S Y,Higgins W E.Symmetric region growing[J].Image Processing,2003,12/9) : 1007-1015.
  • 3Duarte A, Sanchez A, Fernandez F, et al.Improving image segmentation quality through effective region merging using a hierarchical social metaheuristic[J].Pattern Recognition Let- ters, 2006,27 : 1239-1251.
  • 4Xu Zhengdong, Yuan Kui, He Wenhao.An implementation method of Canny edge detection algorithm on FPGA[J].Elec- tric Information and Control Engineering,2011:3958-3962.
  • 5Li Xiangru, Hu Zhanyi, Wu Fuchao.A note on the conver-gence of the mean shift[J].Elsevier Science, 2007, 40(67 : 1756-1762,.
  • 6王新华,毕笃彦.Mean Shift算法在图像分割中的应用研究[J].微计算机信息,2009(9):290-292. 被引量:6
  • 7Tamura H,Mori S,Uamawaki T.Textural features correspond- ing to visual perceptinn[J].lEEE Transactions on Systems, Man and Cybernetics, 1978,8(6) :460-473.
  • 8Gao Zhanguo,Yao Li, Duan Fengyu.Image segmentation algo- rithm based on feature fusion and cluster[C]//2011 Interna- tional Conference on Mechatronic Science,Electric Engineer- ing and Computer,2011 : 1086-1089.
  • 9Comaniciu D,Meer RMean Shift:a robust approach toward feature space analysis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002,24 ( 5 ) : 603-619.

二级参考文献5

  • 1王爽,段红,黄友锐.基于改进的活动轮廓模型在图像分割中的应用[J].微计算机信息,2008,24(1):274-275. 被引量:12
  • 2Fukunaga K, Hostetler L D. The estimation of the gradient of a density function with applications in pattern recognition [J]. IEEE Trans on Information Theory, 1975, 21(1): 32-40
  • 3Cheng Y Z. Mean Shift, mode seeking, and clustering [J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 1995, 17(8): 790-799.
  • 4D Comaniciu, P Meer. Mean Shift:A Robust Approach Toward Feature Space Analysis [J].IEEE Transactions on Patten Analysis and Machine Intelligence,2002;24(5)
  • 5陈兆学,郑建立,施鹏飞.基于Mean Shift方法的视频车辆检测与分割[J].上海理工大学学报,2007,29(2):195-199. 被引量:5

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